傳統的室內定位系統通常要求高成本、高功耗和高複雜度來保持準確度的
效能。除此之外，這些系統也容易受到環境的影響。因此我們想要設計一個可
以解決上述問題的室內定位系統。
在本篇論文中，我們提出一個適應性室內定位系統，當環境改變時，它可
以更新接收訊號強度(Received signal strength indication)地圖。這個系統結合了接收訊號強度指紋(Received signal strength indication-fingerprint)與單位原點(Cell of origin)的混合式系統。在系統中，使用平面內插的方式來更新接收訊號強度地圖，除了這樣的改善，還改善當目標處於非直視(Non-line-of-sight)下的準確度以及系統整體的計算時間。我們使用接收訊號強度判別非直視情況並且進行訊號補償，並且利用 K 平均演算法(K-mean clustering algorithm)來減少計算時間。
本篇論文提出的系統架構是由藍牙低功耗標籤(BLE tag)、藍牙低功耗/無線
網路中繼器(BLE/Wi-Fi repeater)和伺服器(Server)所構成。藍牙低功耗標籤負責
廣播藍牙信標，中繼器內部具有五個藍牙低功耗掃描器，負責萃取出接收訊號
強度，萃取出的訊號強度透過無線網路(Wi-Fi)上傳到伺服器，之後伺服器利用
訊號強度這些資訊來估測標籤的位置。除此之外，藍牙低功耗標籤大小半徑是
1.7 公分，厚度是 0.5 公分，每個藍牙低功耗標籤的成本是 3 美元。功耗的部
分，標籤消耗的平均電流是 266 微安培，搭配 CR2025 鋰電池可以連續使用 30
天左右。
為了驗證系統的效能，我們在實際環境中測試。根據實驗結果，適應性的
系統可以改善大約 20%的準確度以及降低在訓練階段前處理的複雜度。在空曠
環境以及複雜環境中，綜合目標被阻擋(Blocked)以及沒被阻擋(Non-blocked)的
平均定位的誤差是 1.93 公尺以及 2.34 公尺。此外系統整體的計算時間在不同環
境中分別改善了 52.5%以及 75.3%的時間。

The traditional indoor positioning system usually needs high cost, high power consumption and high complexity to keep the performance of accuracy. Besides, it is easily affected by the variation of environment. Therefore, we want to design an indoor positioning that can solve above problems.
In the thesis, we have proposed an adaptive indoor positioning system that can update received signal strength indication (RSSI) map when the environment changes. The system is a hybrid system from RSSI-fingerprint and cell of origin (CoO). In our system, we have used plane-interpolate to update RSSI map. Besides the above improvement, We also have improved the accuracy of the non-line-of-sight (NLOS) situation and reduced the calculation time of the overall system. We have used RSSI to determine whether the NLOS situation and compensated RSSI, and used k-mean clustering algorithm to reduce the calculation time.
The proposed architecture of the indoor positioning system consists of a server, BLE tags and BLE/Wi-Fi repeaters that there are five BLE scanners inside a BLE/Wi-Fi repeater. The BLE tag as the target in the our system which broadcasts BLE beacons. The size of the BLE tag is 1.7 cm in radius and 0.5 cm thick. The cost of each tag is about 3 USD. The BLE tag can without charge for about 30 days with a CR2025 battery and the current consumption is 266 µA. The BLE/Wi-Fi repeaters collected BLE beacons from BLE tags and extract the RSSI and tag’s ID. Since a BLE/Wi-Fi with five BLE scanners, we can receive multiple BLE beacons and extract multiple RSSI at the same time. The RSSI values are transmitted to server thought Wi-Fi, and the server will use this information from BLE/Wi-Fi repeaters to estimate the position of the BLE tag with RSSI-based positioning algorithms.
To verify the performance of our indoor positioning system, we will test our indoor positioning in real situation. According to our experiment, the result of adaptive system improved by about 20.73% accuracy. Besides, we find the relation between adaptive system and complexity of pre-processing. If we use the proposed adaptive scheme, we only collect 10 RSSI raw data to be averaged in pre-processing in the training phase. The mean error distance of the blocked position and unblocked position are 2.66 m and 1.19 m and the total mean error distance is 1.93 m in the rest area. The mean error distance of the blocked position and unblocked position are 3.20 m and 1.47 m and the total mean error distance is 2.34 m in the office area. Moreover, the calculation time of the overall system is reduced by 52.5% and 75.3% at the rest and the office area.

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